Thanks!
> > ### this is the loop I would like to optimize:
> > ### looping over arrays is considered inefficient.
> > ### what could be a better way?
> > hours_array = dates_array.copy()
> > for i in range(0, dates_array.size):
> > hours_array[i] = dates_array[i].hour
>> You could try:
> np.fromiter((_.hour for _ in dates_li), dtype=np.int)
> or
> np.array([_.hour for _ in dates_li], dtype=np.int)
I used dates_li only for the preparation of example data.
So let's suppose I have the array "dates_array" returned from a
a function.
How can the last part be improved:
hours_array = dates_array.copy()
for i in range(0, dates_array.size):
hours_array[i] = dates_array[i].hour
Or is such a loop accepable from the point of calculation efficiency?
Thanks and greetings,
Timmie